Cholesterol, lipoproteins and subclinical interstitial lung disease: The MESA study

Anna J. Podolanczuk, Ganesh Raghu, Michael Y. Tsai, Steven M. Kawut, Eric Peterson, Rajiv Sonti, Daniel Rabinowitz, Craig Johnson, R. Graham Barr, Karen Hinckley Stukovsky, Eric A. Hoffman, J. Jeffrey Carr, Firas S. Ahmed, David R. Jacobs, Karol Watson, Steven J. Shea, David J. Lederer

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

We investigated associations of plasma lipoproteins with subclinical interstitial lung disease (ILD) by measuring high attenuation areas (HAA: lung voxels between -600 and -250 Hounsfield units) in 6700 adults and serum MMP-7 and SP-A in 1216 adults age 45-84 without clinical cardiovascular disease in Multi-Ethnic Study of Atherosclerosis. In cross-sectional analyses, each SD decrement in high density lipoprotein cholesterol (HDL-C) was associated with a 2.12% HAA increment (95% CI 1.44% to 2.79%), a 3.53% MMP-7 increment (95% CI 0.93% to 6.07%) and a 6.37% SP-A increment (95% CI 1.35% to 11.13%), independent of demographics, smoking and inflammatory biomarkers. These findings support a novel hypothesis that HDL-C might influence subclinical lung injury and extracellular matrix remodelling.

Original languageEnglish (US)
Pages (from-to)472-474
Number of pages3
JournalThorax
Volume72
Issue number5
DOIs
StatePublished - Jan 27 2017

Bibliographical note

Funding Information:
The work is funded by the National Institutes of Health contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168 and N01-HC-95169 and grants UL1-TR-000040, UL1-TR-001079, R01-HL-103676, RC1-HL100543, R01-HL-093081, R01-HL-077612, T32-HL-105323 and K24-HL-131937; by the Pulmonary Fibrosis Foundation; and by the Rocco Guinta Research Fund.

Fingerprint Dive into the research topics of 'Cholesterol, lipoproteins and subclinical interstitial lung disease: The MESA study'. Together they form a unique fingerprint.

Cite this